Covid Report for Family

Background

Notes and format last updated May 7, 2020

Starting on the May 7th update, the NY Times began including probable covid cases/deaths along with confirmed. This mostly affects death counts – for certain geographies that include probable COVID deaths in addition to confirmed, these are now added to the totals. For the time being, they were all added to the May 6th totals, causing a big spike at the U.S. level. Over time, NY Times will revise their historical counts and distribute these added deaths when they actually occurred, so the spike should fade.

• This page shows COVID data broken down into regions that affect our family. It pulls from publicly available raw data from the NY Times, which is reported daily. It’s on a one-day lag, so when you see this report, the most recent day included will be that reported the day prior.
• The table of contents above summarizes the major sections and can be used to jump to those sections.
• Each major section is then divided into tabs to allow you to more easily shuffle between geographies and metrics.
  • If you open on your phone, I don’t believe the tabs will work, so you’ll just see the page as one long, continuous document.
• More detailed descriptions and context can be found in each section.

Growth rates

Heat maps

• The two heat maps below compare how quickly total cases or deaths have grown at various times in our respective geopgraphies.
• The first plot compares growth rate for total cases; the second, growth rate for total deaths.
• The metric used is doubling time, by which I mean how quickly total cases or deaths are doubling.
• The plots track that doubling time at each date for our geographies. Darker colors reflect shorter doubling times, and thus periods of faster growth.
  • You can use the plots to track each geography over time and to compare the geographies to one another.
  • You can also compare the cases and death charts, to see how faster periods of death growth follow faster periods of case growth.

Case growth rates

• This section charts the growth rate of both total and new cases for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total cases and new cases.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of cases, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new cases) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new case reporting at the state level.
  • For total cases, the trendlines are a rolling 3-day average of daily growth rates in total cases. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total cases have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total case line get as close to zero as possible.
  • For new cases, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new cases. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total cases, we want to watch for the lines for new cases to get consistently below zero and stay there. That means that we are consistently seeing fewer new cases on a daily basis.

U.S.

Our states

Death growth rates

• This section charts the growth rate of both total and new deaths for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total deaths and new deaths.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of deaths, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new deaths) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new death reporting at the state level.
  • For total deaths, the trendlines are a rolling 3-day average of daily growth rates in total deaths. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total deaths have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total death line get as close to zero as possible.
  • For new deaths, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new deaths. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total deaths, we want to watch for the lines for new deaths to get consistently below zero and stay there. That means that we are consistently seeing fewer new deaths on a daily basis.

U.S.

Our states

By population rankings

This section tracks metrics for states and counties normalized for population (number of cases or deaths per million residents), and then compares these figures both for our geographies and the country overall.

States

• This section shows tables ranking all 50 states for per populations rates of total cases, new cases, total deaths, and new deaths.
• For each metric, in addition to the tables, the trends for the top states are plotted over time.
  • We only plot the top ten states for each metric so that the plots aren’t too crowded. But you can view the full 50-state rankings in the tables.

Total confirmed cases

Table of total confirmed cases per million residents (all 50 states)

Ranking	State	Cases Per Million
1	North Dakota	141,450
2	Rhode Island	140,338
3	South Dakota	138,748
4	Utah	124,238
5	Tennessee	122,231
6	Arizona	118,781
7	Iowa	115,924
8	Nebraska	113,846
9	Wisconsin	113,746
10	Oklahoma	113,438
11	South Carolina	112,803
12	New Jersey	112,696
13	Arkansas	111,394
14	Indiana	107,952
15	Delaware	107,897
16	Alabama	107,845
17	Kansas	106,996
18	Illinois	106,179
19	New York	105,660
20	Idaho	105,256
21	Mississippi	104,967
22	Florida	104,566
23	Minnesota	102,920
24	Nevada	102,669
25	Montana	102,181
26	Georgia	101,642
27	Wyoming	100,715
28	Kentucky	100,438
29	Massachusetts	100,360
30	Texas	100,064
31	Louisiana	98,808
32	Missouri	98,253
33	Connecticut	95,516
34	California	94,834
35	Michigan	94,615
36	New Mexico	94,605
37	North Carolina	93,550
38	Alaska	92,778
39	Ohio	92,051
40	Pennsylvania	90,888
41	Colorado	89,852
42	West Virginia	86,045
43	Virginia	77,549
44	Maryland	74,383
45	New Hampshire	70,239
46	District of Columbia	67,875
47	Washington	53,730
48	Puerto Rico	52,184
49	Maine	46,192
50	Oregon	44,318
51	Vermont	37,112
52	Hawaii	23,349

New confirmed cases

Table of new cases per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Cases Per Million
1	Michigan	304
2	Minnesota	267
3	Delaware	226
4	Maine	217
5	Colorado	213
6	West Virginia	200
7	Florida	191
8	Pennsylvania	190
9	Washington	182
10	Illinois	178
11	Puerto Rico	168
12	Oregon	164
13	New York	156
14	New Hampshire	151
15	Rhode Island	151
16	Indiana	149
17	New Jersey	141
18	South Carolina	138
19	North Carolina	135
20	Massachusetts	133
21	North Dakota	126
22	Alaska	124
23	Connecticut	122
24	Maryland	116
25	Missouri	114
26	Vermont	110
27	Ohio	105
28	Georgia	104
29	New Mexico	101
30	District of Columbia	97
31	Iowa	96
32	Arizona	95
33	Utah	95
34	Kentucky	93
35	Tennessee	92
36	Wisconsin	92
37	Virginia	91
38	Montana	86
39	Nevada	86
40	Texas	85
41	Wyoming	85
42	Idaho	69
43	Hawaii	68
44	Nebraska	68
45	Alabama	58
46	Kansas	57
47	Mississippi	56
48	Louisiana	54
49	Arkansas	48
50	Oklahoma	47
51	California	43
52	South Dakota	32

Total deaths

Table of total deaths per million residents (all 50 states)

Ranking	State	Deaths Per Million
1	New Jersey	2,883
2	New York	2,667
3	Massachusetts	2,559
4	Rhode Island	2,527
5	Mississippi	2,421
6	Arizona	2,382
7	Connecticut	2,275
8	Louisiana	2,235
9	South Dakota	2,226
10	Alabama	2,225
11	Pennsylvania	2,056
12	North Dakota	2,001
13	Indiana	1,983
14	New Mexico	1,942
15	Illinois	1,924
16	Arkansas	1,904
17	Michigan	1,893
18	Iowa	1,888
19	South Carolina	1,849
20	Georgia	1,846
21	Nevada	1,776
22	Tennessee	1,772
23	Texas	1,741
24	Kansas	1,726
25	Oklahoma	1,715
26	Delaware	1,669
27	Ohio	1,649
28	Florida	1,643
29	California	1,574
30	District of Columbia	1,567
31	Missouri	1,507
32	West Virginia	1,498
33	Kentucky	1,486
34	Montana	1,472
35	Maryland	1,455
36	Wisconsin	1,299
37	Minnesota	1,286
38	Virginia	1,266
39	Nebraska	1,225
40	Wyoming	1,221
41	North Carolina	1,212
42	Idaho	1,149
43	Colorado	1,118
44	New Hampshire	959
45	Washington	729
46	Puerto Rico	724
47	Utah	687
48	Oregon	597
49	Maine	587
50	Alaska	452
51	Vermont	397
52	Hawaii	339

New deaths

Table of new deaths per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Deaths Per Million
1	Michigan	5
2	California	4
3	Florida	2
4	Illinois	2
5	Maryland	2
6	New Jersey	2
7	New York	2
8	Rhode Island	2
9	Alabama	1
10	Connecticut	1
11	Georgia	1
12	Idaho	1
13	Kentucky	1
14	Maine	1
15	Massachusetts	1
16	Minnesota	1
17	North Carolina	1
18	Pennsylvania	1
19	Puerto Rico	1
20	South Carolina	1
21	South Dakota	1
22	Vermont	1
23	Virginia	1
24	West Virginia	1
25	Alaska	0
26	Arizona	0
27	Arkansas	0
28	Colorado	0
29	Delaware	0
30	District of Columbia	0
31	Hawaii	0
32	Indiana	0
33	Iowa	0
34	Kansas	0
35	Louisiana	0
36	Mississippi	0
37	Missouri	0
38	Montana	0
39	Nebraska	0
40	Nevada	0
41	New Hampshire	0
42	New Mexico	0
43	North Dakota	0
44	Ohio	0
45	Oklahoma	0
46	Oregon	0
47	Tennessee	0
48	Texas	0
49	Utah	0
50	Washington	0
51	Wisconsin	0
52	Wyoming	0

Counties

• This section focuses on the county level. It shows tables with our counties ranked by percentile of U.S. counties for per population rates of total cases and total deaths.
  • Each table also shows the top five counties in the country in addition to our counties, for added perspecive.
• In addition to the tables, our counties’ percentile for both total cases and total deaths are plotted over time.

Confirmed cases

Table showing total cases per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Cases Per Million	Raw Ranking	Percentile
Chattahoochee	Georgia	361,511	1	99
Crowley	Colorado	360,007	2	99
Bent	Colorado	273,265	3	99
Dewey	South Dakota	248,982	4	99
Lincoln	Arkansas	245,700	5	99
Davidson	Tennessee	141,726	184	94
Richland	South Carolina	111,040	981	68
York	South Carolina	109,699	1039	66
Orange	California	85,129	2187	30
Pierce	Washington	55,127	2866	8

Our county percentiles over time

Deaths

Table showing total deaths per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Deaths Per Million	Raw Ranking	Percentile
Foard	Texas	8,658	1	99
Galax city	Virginia	8,035	2	99
Jerauld	South Dakota	7,948	3	99
Emporia city	Virginia	7,856	4	99
Hancock	Georgia	7,686	5	99
Orange	California	1,565	1823	41
York	South Carolina	1,342	2081	33
Davidson	Tennessee	1,341	2083	33
Richland	South Carolina	1,313	2113	32
Pierce	Washington	719	2742	12

Our county percentiles over time

Raw counts

• This section shows the raw counts over time for the U.S., our states, and our counties for four categories:
  • Total confirmed cases: This is the cumulative total of cases reported.
  • New confirmed cases: This is the number of new cases reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new cases, to help better visualize the peak.
  • Total deaths: This is the cumulative total of deaths reported.
  • New deaths: This is the number of new deaths reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new deaths, to help better visualize the peak.

Total confirmed cases

U.S.

Our states

Our counties

New confirmed cases

U.S.

Our states

Our counties

Total deaths

U.S.

Our states

Our counties

New deaths

U.S.

Our states

Our counties

Stay-at-home comparisons

• This section is a work in progress. It’s exploring what effect stay-at-home orders have on spread. It’s inherentely messy because some states have localized orders but not orders for their full state and because states implemented the orders at different times.
  • The first plot shows a rolling 3-day average of new cases per million residents, averaged across 2 groups of states – those with a state-wide stay-at-home order in effect and those without. The size of the dots reflect the number of states in each group for each given day (so the dots are getting bigger over time for the group with orders, as more states implement those orders).
    • So far, this view doesn’t show if these orders are “working” or not. Instead, the biggest takeaway from the current plot is that the states that implemented orders are reacting to having worse spread, which is why the case rate is so much higher at the start for them. As a state decides it has a problem and implements an order, it joins the grouping of states with orders and drives the rate up for that group.
    • Over time (if the policies work), what we’d expect is that the line for the group with orders flattens or declines in relation to the no-order group. It also may become moot if all states issue orders.
    • As states start rescending their orders, I’ll put them back into the no-order group, so the no-order group should beging to expand again.
  • The second plot tracks progress of states by the number of days since orders have been in effect. It includes only those states with orders and shows their average new case growth rate (a rolling 3-day average of daily growth rates) based on how long it’s been since the order went into effect. Again, the size of the dots show how many states are included in each point – i.e., as you go further right, the dots are smaller because less states are that far past their order.
    • I’m working on how to add a baseline to this chart to compare to states without orders, but that’s challenging because there’s no way to fully align timing.